Data Mining | Week 8

Data Mining Week 8 Answers

link: Data Mining Week (nptel.ac.in)

Q1. Target variable in Regression is _
a. Continuous variable
b. Discrete variable
c. Character variable
d. All of the above

Q2. Regression is used in:
a. predictive data mining
b. exploratory data mining
c. descriptive data mining
d. explanative data mining

Q3. Regression finds out the model parameters which produces the least square error between –
a. input value and output value
b. input value and target value
c. output value and target value
d. model parameters and output value

Q4. Consider x1, x2 to be the independent variables and y to be the dependent variable, which of the following represents a linear regression model?
a. y = a0 + a1/x1 + a2/x2
b. y = a0 + a1x1 + a2x2
c. y = a0 + a1x1 + a2x22
d. y = a0 + a1x12 + a2x2

Q5. The linear regression model y = a₀ + a₁x is applied to the data in the table shown below. What is the value of the sum squared error function S(a₀, a₁), when a₀ = 1, a₁=2 ?

a. 0.00
b. 0.25
c. 0.50
d. 0.51

Q6. The linear regression model y = a₀ + a₁x is to be fitted to the data in the table shown below. What is the optimal regression model obtained by minimizing sum squared error?

a. y = 1.01 –2.10x
b. y = 1.01 +2.10x
c. y = 1.01 – 0.98x
d. y = 1.01 + 0.98x

Q7. In the figures below the training instances are described by dots. The blue dotted lines indicate the actual functions and the red lines indicate the regression model. Which of the following statement is correct?

a. Figure 1 represents overfitting and Figure 2 represents underfitting
b. Figure 1 represents underrfitting and Figure 2 represents overfitting
c. Both Figure 1 and Figure 2 represents underfitting
d. Both Figure 1 and Figure 2 represents overfitting

Q8. Find the eigenvalues of the following matrix

a. 1,3
b. 2,3
c. 1,2,3
d. Eigenvalues cannot be found.

Q9. A time series prediction problem is often solved using?
a. Multivariate regression
b. Autoregression
c. Logistic regression
d. Sinusoidal regression

Q10. In principal component analysis, the projected lower dimensional space corresponds to –
a. subset of the original co-ordinate axis
b. eigenvectors of the data covariance matrix
c. eigenvectors of the data distance matrix
d. orthogonal vectors to the original co-ordinate axis